ArguteDUB: deep learning based distributed uplink beamforming in 6G-based IoV

Yi, X, Li, J, Liu, Y, Kong, L, Shao, Y, Chen, G, Liu, X, Mumtaz, S ORCID: https://orcid.org/0000-0001-6364-6149 and Rodrigues, JJPC, 2023. ArguteDUB: deep learning based distributed uplink beamforming in 6G-based IoV. IEEE Transactions on Mobile Computing. ISSN 1536-1233

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Abstract

In the last decade, MIMO spatial multiplexing and distributed beamforming play a significant role in improving datathroughput through cooperative transmission. It has been widely used in wireless communication, especially in 6G. However, thedistributed uplink beamforming is still an open problem in highly dynamic environments. However, the proposed 6G technologyrepresents the further integration of deep learning and wireless communication. In this paper, we propose Argute Distributed UplinkBeamforming (ArguteDUB), which uses a feedback algorithm with an offline-trained deep learning model to implement highly dynamicdistributed uplink beamforming for the Internet of Vehicles (IoV) in 6G. Specifically, each vehicle enables the base station (BS)/accesspoint (AP) to separate different channel state information (CSI) by inserting orthogonal sequences into the sending data. The BSadopts deep learning to filter the noise and predict the beamforming weight to achieve phase synchronization. Unlike traditionaldistributed uplink beamforming, ArguteDUB can be adapted to the highly dynamic time-varying channels. The simple network structureensures the fast response of ArguteDUB. In addition, we make ArguteDUB Orthogonal Frequency Division Multiplexing (OFDM)compatible so that it can be easily deployed in 6G networks. Our evaluation shows that ArguteDUB has an signal-to-noise ratio (SNR)gain of about 5dB to 5.3dB over the single vehicle transmission mode

Item Type:	Journal article
Publication Title:	IEEE Transactions on Mobile Computing
Creators:	Yi, X., Li, J., Liu, Y., Kong, L., Shao, Y., Chen, G., Liu, X., Mumtaz, S. and Rodrigues, J.J.P.C.
Publisher:	Institute of Electrical and Electronics Engineers (IEEE)
Date:	28 March 2023
ISSN:	1536-1233
Identifiers:	Number Type 10.1109/tmc.2023.3262320 DOI 1746837 Other
Rights:	© 2023 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Divisions:	Schools > School of Science and Technology
Record created by:	Laura Ward
Date Added:	13 Apr 2023 08:12
Last Modified:	13 Apr 2023 08:12
URI:	https://irep.ntu.ac.uk/id/eprint/48719

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